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考虑了一类具有零动态的非仿射非线性不确定系统的神经网络直接自适应跟踪控制问题.控制信号由神经网络系统直接产生,无需另外设计系统估计器及鲁棒控制项.采用梯度下降方法以最小化神经网络控制器与未知理想控制器的误差代价函数产生神经网络白适应参数更新律.应用Lyapunov方法证明了闭环系统的稳定性及跟踪误差和相应闭环系统的所有状态最终一致有界性.最后针对带有外部扰动的非仿射非线性系统的仿真结果验证了该文方法的有效性.
Consider a kind of neural network direct adaptive tracking control problem with non-state-dependent non-affine nonlinear uncertain systems. The control signals are generated directly by the neural network system without the need of separately designing system estimators and robust control terms. The method is based on Lyapunov method to prove that the stability and tracking error of the closed-loop system and all the states of the corresponding closed-loop system are finally uniform and bounded Finally, the simulation results of the non-affine nonlinear system with external disturbances verify the effectiveness of the proposed method.